{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d0bb2505",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "import csv\n",
    "import os "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c37bbcbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Cost(tht1, tht2):\n",
    "    n = X.shape[0]\n",
    "    cost = 0.0\n",
    "    for i in range(n):\n",
    "        cost += (y[i] - tht1*X[i][0] - tht2*X[i][1])**2 \n",
    "    return cost/n\n",
    "\n",
    "def grad(tht):\n",
    "    n = X.shape[0]\n",
    "    gC = -2*(y - X@tht).T@X    \n",
    "    return gC/n\n",
    "\n",
    "def GradientDescent(grad, tht, alpha, max_iter = float('inf'), epsilon = 1e-5):    \n",
    "    t = tht\n",
    "    t_seq = [t]\n",
    "\n",
    "    stop = False\n",
    "    iter = 0\n",
    "    while not stop:\n",
    "        step = -alpha*grad(t)\n",
    "        t = t + step\n",
    "        t_seq.append(t)\n",
    "        iter = iter + 1\n",
    "        if np.linalg.norm(step) < epsilon or iter == max_iter:\n",
    "            stop = True        \n",
    "    t_seq = np.array(t_seq)\n",
    "    return t_seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3aeb5d49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_samples=490\n"
     ]
    }
   ],
   "source": [
    "#Description from Kaggle of the Boston dataset (506 observations)\n",
    "# [0] - CRIM per capita crime rate by town \n",
    "# [1] - ZN proportion of residential land zoned for lots over 25,000 sq.ft. \n",
    "# [2] - INDUS proportion of non-retail business acres per town \n",
    "# [3]- CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) \n",
    "# [4]- NOX nitric oxides concentration (parts per 10 million) \n",
    "# [5]- RM average number of rooms per dwelling \n",
    "# [6]- AGE proportion of owner-occupied units built prior to 1940 \n",
    "# [7]- DIS weighted distances to five Boston employment centres \n",
    "# [8]- RAD index of accessibility to radial highways \n",
    "# [9]- TAX full-value property-tax rate per $10,000 \n",
    "# [10]- PTRATIO pupil-teacher ratio by town \n",
    "# [11]- B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town \n",
    "# [12]- LSTAT % lower status of the population \n",
    "# [13]- MEDV Median value of owner-occupied homes in $1000's\n",
    "\n",
    "n_samples=506\n",
    "X =  np.zeros((n_samples,2))\n",
    "y = np.zeros(n_samples)\n",
    "\n",
    "with open('../data/housing.csv') as csvfile:\n",
    "    reader = csv.reader(csvfile)\n",
    "    i = 0\n",
    "    for row in reader:\n",
    "        lst = [float(j) for j in ', '.join(row).split()]\n",
    "        rm_var = lst[5] \n",
    "        medv_var = lst[13]\n",
    "        if medv_var < 50.0: #Truncation value\n",
    "            X[i, 0] = 1\n",
    "            X[i, 1] = rm_var\n",
    "            y[i] = medv_var\n",
    "            i = i +1\n",
    "\n",
    "n_samples = i\n",
    "X = X[0:n_samples,:]\n",
    "y = y[0:n_samples]\n",
    "print(f\"{n_samples=}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fd97e3f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "beta0 = -30.005120145375855, beta1 = 8.268557322426984\n"
     ]
    }
   ],
   "source": [
    "theta_explicit = np.matmul(np.linalg.pinv(X),y)\n",
    "print(f\"beta0 = {theta_explicit[0]}, beta1 = {theta_explicit[1]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5badb8ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "x_data = np.random.normal(6, 2.5, n_samples)\n",
    "X[:,1] = x_data\n",
    "y = theta_explicit[0] + theta_explicit[1]*x_data + np.random.normal(0,3,n_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "74957b6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "beta0 = -30.448388600049572, beta1 = 8.310100644523807\n"
     ]
    }
   ],
   "source": [
    "theta_explicit = np.matmul(np.linalg.pinv(X),y)\n",
    "print(f\"beta0 = {theta_explicit[0]}, beta1 = {theta_explicit[1]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e52a354c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# n_samples = 5\n",
    "# X = np.ones((n_samples,2))\n",
    "# # x_data = [1.1,2.3,2.8,4.02,5.3]\n",
    "# y_data = [3.9,5.15,6.1,9.98,8.03]\n",
    "# X[:,1] = x_data\n",
    "# y = y_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3abee9a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "beta0 = -30.448388600049572, beta1 = 8.310100644523807\n"
     ]
    }
   ],
   "source": [
    "theta_explicit = np.matmul(np.linalg.pinv(X),y)\n",
    "print(f\"beta0 = {theta_explicit[0]}, beta1 = {theta_explicit[1]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7d8c4889",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5.928320085963393, 2.4871677759130955)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mu_x = np.mean(X,axis=0)[1]\n",
    "sig_x = np.std(X,axis=0)[1]\n",
    "(mu_x,sig_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c4f35397",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18.816547967258238, 20.88095757738944)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mu_y = np.mean(y)\n",
    "sig_y = np.std(y)\n",
    "(mu_y, sig_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "899b889e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Standardize X and Y\n",
    "# X[:,1] = (X[:,1]-mu_x)/sig_x\n",
    "# y = (y - mu_y)/sig_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a5de45d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lambda_max=20670.326800608345\n",
      "alpha_bound=0.02370547910183882\n",
      "theta_explicit=array([-30.4483886 ,   8.31010064])\n"
     ]
    }
   ],
   "source": [
    "lambda_max = abs(np.linalg.eigvals(np.matmul(X,X.T)))[0] \n",
    "print(f\"{lambda_max=}\")\n",
    "alpha_bound = n_samples/lambda_max\n",
    "print(f\"{alpha_bound=}\")\n",
    "print(f\"{theta_explicit=}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f10a54c",
   "metadata": {},
   "source": [
    "$$\n",
    "\\frac{y-\\mu_y}{\\sigma_y} = \\beta_0 + \\beta_1 \\frac{x-\\mu_x}{\\sigma_x} = \\beta_0 -\\frac{\\beta_1 \\mu_x}{\\sigma_x}+ \\frac{\\beta_1}{\\sigma_x}x \n",
    "$$\n",
    "\n",
    "$$\n",
    "y = \\sigma_y\\beta_0 -\\frac{\\beta_1 \\sigma_y \\mu_x}{\\sigma_x} + \\mu_y  + \\frac{\\sigma_y \\beta_1}{\\sigma_x}x\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "46335062",
   "metadata": {},
   "outputs": [],
   "source": [
    "# unshifted_beta_0 = sig_y*gd_min[0]  -gd_min[1]*sig_y*mu_x/sig_x + mu_y\n",
    "# unshifted_beta_1 = sig_y*gd_min[1]/sig_x\n",
    "# unshifted_beta_0, unshifted_beta_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9bd78bf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gd_min=array([-30.44502456,   8.3096164 ])\n",
      "3109\n"
     ]
    }
   ],
   "source": [
    "alpha_A = 0.01# learning rate\n",
    "t_init = np.array([0,0]) # initial point\n",
    "t_seq_gd_A = GradientDescent(grad, t_init, alpha_A,max_iter=10e4)\n",
    "gd_min = t_seq_gd_A[-1,:]\n",
    "print(f\"{gd_min=}\")\n",
    "print(len(t_seq_gd_A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "95a15d86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gd_min=array([-30.44697218,   8.30989676])\n",
      "1446\n"
     ]
    }
   ],
   "source": [
    "alpha_B = 0.0235# learning rate\n",
    "t_init = np.array([0,0]) # initial point\n",
    "t_seq_gd_B = GradientDescent(grad, t_init, alpha_B,max_iter=10e4)\n",
    "gd_min = t_seq_gd_B[-1,:]\n",
    "print(f\"{gd_min=}\")\n",
    "print(len(t_seq_gd_B))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "10c4b289",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-6-8a4d4a9d9005>:10: RuntimeWarning: invalid value encountered in matmul\n",
      "  gC = -2*(y - X@tht).T@X\n",
      "<ipython-input-6-8a4d4a9d9005>:21: RuntimeWarning: invalid value encountered in add\n",
      "  t = t + step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gd_min=array([nan, nan])\n",
      "100001\n"
     ]
    }
   ],
   "source": [
    "alpha_C = 0.024# learning rate\n",
    "t_init = np.array([0,0]) # initial point\n",
    "t_seq_gd_C = GradientDescent(grad, t_init, alpha_C,max_iter=10e4)\n",
    "gd_min = t_seq_gd_C[-1,:]\n",
    "print(f\"{gd_min=}\")\n",
    "print(len(t_seq_gd_C))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "78a8dd98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t1_min = -40\n",
    "t1_max = 5\n",
    "t2_min = -5\n",
    "t2_max = 15\n",
    "t1_range = np.arange(t1_min, t1_max, 0.1)\n",
    "t2_range = np.arange(t2_min, t2_max, 0.1)\n",
    "t1_arr, t2_arr = np.meshgrid(t1_range, t2_range)\n",
    "\n",
    "Z = Cost(t1_arr, t2_arr)\n",
    "\n",
    "fig, ax = plt.subplots()  \n",
    "ax.contour(t1_range, t2_range, Z, linewidths=1, levels=np.exp(np.arange(0, 20, 0.6)))#, cmap='Blues')\n",
    "ax.plot(t_seq_gd_B[:, 0], t_seq_gd_B[:, 1], '-or', label=r'$\\alpha = 0.0235$' + f', {len(t_seq_gd_B)} iterations', alpha=1, linewidth=1, markersize=0,color='blue')\n",
    "ax.plot(t_seq_gd_A[:, 0], t_seq_gd_A[:, 1], '-or', label=r'$\\alpha = 0.01$' + f', {len(t_seq_gd_A)} iterations', alpha=1, linewidth=2, markersize=0,color='red')\n",
    "ax.plot(t_seq_gd_C[:300, 0], t_seq_gd_C[:300, 1], '-or', label=r'$\\alpha = 0.024$' + ', diverges', alpha=0.2, linewidth=1, markersize=0,color='black')\n",
    "ax.plot([0],[0], 'og', markersize=3,color='black')\n",
    "ax.plot([theta_explicit[0]], [theta_explicit[1]], 'og', markersize=5)\n",
    "\n",
    "plt.xlabel(r'$\\beta_0$', fontsize=12)\n",
    "plt.ylabel(r'$\\beta_1$', fontsize=12)\n",
    "plt.legend(loc='lower left')\n",
    "plt.xlim(t1_min,t1_max)\n",
    "plt.ylim(t2_min,t2_max)\n",
    "plt.subplots_adjust(bottom=0.15)\n",
    "\n",
    "fig.savefig(\"/Users/uqjnazar/Dropbox/MathEngDeepLearningBook/LaTeXBook/figures/chapter_2_figures/learning_rate_linear.pdf\")\n",
    "\n",
    "plt.show()\n",
    "fig = plt.figure();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "e8f1144e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bx = plt.axes(projection='3d')\n",
    "# bx.plot_surface(t1_arr, t2_arr, Z, cmap='Blues', edgecolor='none')\n",
    "# bx.set_xlabel(r'$w_1$', fontsize=20)\n",
    "# bx.set_ylabel(r'$w_2$', fontsize=20)\n",
    "# bx.set_zlabel(r'$C(\\theta)$', fontsize=20) \n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7070a719",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
